Multi-Pooling Attention Learning for Melanoma Recognition

Abstract

Melanoma is a type of skin cancer with high mortality rate. Early diagnosis of malignant melanoma usually relies on the judgement of dermatologists. Recently, more and more research paid attention to classify skin lesion automatically by dermoscopy images, which is able to assist clinicians to diagnose quickly and accurately. However, there still remain some challenges on extraction of discriminative feature due to the inter-class similarity and intra-class variation. In this paper, we propose a novel multi-pooling attention learning for skin lesions classification, which consists of multiple types of pooling operations, attention mechanism and feature fusion. The multi-pooling layer with flexible pooling patterns is designed to capture more representative features. The attention mechanism has the ability to help our model focus on the lesion regions. Meanwhile, in order to deal with the issues of inter-class similarity and intra-class variation of skin lesions, we adopt a joint loss function to optimize the final results. Our proposed approach has been validated on ISIC2017 dataset that provided by IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge for Skin Lesion Analysis Towards Melanoma Detection. The results show that our proposed network achieves superior performance over six top-ranking approaches in ISBI 2017 challenge leaderboard and five other methods.